We propose an outdoor localization system using a particle filter. In our approach, a textured, geo-registered model of the outdoor environment is used as a reference to estimate the pose of a smartphone. The device position and the orientation obtained from a Global Positioning System (GPS) receiver and an inertial measurement unit (IMU) are used as a first estimation of the true pose. Then, multiple pose hypotheses are randomly distributed about the GPS/IMU measurement and use to produce renderings of the virtual model. With vision-based methods, the rendered images are compared with the image received from the smartphone, and the matching scores are used to update the particle filter. The outcome of our system improves the camera pose estimate in real time without user assistance.